WO2023003979A3 - Optimal data-driven decision-making in multi-agent systems - Google Patents
Optimal data-driven decision-making in multi-agent systems Download PDFInfo
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- WO2023003979A3 WO2023003979A3 PCT/US2022/037763 US2022037763W WO2023003979A3 WO 2023003979 A3 WO2023003979 A3 WO 2023003979A3 US 2022037763 W US2022037763 W US 2022037763W WO 2023003979 A3 WO2023003979 A3 WO 2023003979A3
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
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- G06N5/00—Computing arrangements using knowledge-based models
- G06N5/04—Inference or reasoning models
- G06N5/043—Distributed expert systems; Blackboards
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N20/00—Machine learning
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F3/00—Input arrangements for transferring data to be processed into a form capable of being handled by the computer; Output arrangements for transferring data from processing unit to output unit, e.g. interface arrangements
- G06F3/01—Input arrangements or combined input and output arrangements for interaction between user and computer
- G06F3/011—Arrangements for interaction with the human body, e.g. for user immersion in virtual reality
- G06F3/015—Input arrangements based on nervous system activity detection, e.g. brain waves [EEG] detection, electromyograms [EMG] detection, electrodermal response detection
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- G—PHYSICS
- G06—COMPUTING OR CALCULATING; COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/004—Artificial life, i.e. computing arrangements simulating life
- G06N3/006—Artificial life, i.e. computing arrangements simulating life based on simulated virtual individual or collective life forms, e.g. social simulations or particle swarm optimisation [PSO]
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Abstract
Systems and methods for optimizing data-driven decision-making in multi-agent systems are described. The system may construct an equilibrium concept to capture multi-layer and/or k-level depth reasoning by agents. The system may determine best-response type conjectures for agents to interact with one another. In some examples, the system may include a machine or an algorithm interacting with a strategic agent (e.g., a human or an entity). The system may include methods for: (1) data-driven estimation and/or learning of conjectures and associated depth; and (2) data-driven design of algorithmic mechanisms for exploring the conjectural equilibrium (CE) space by influencing the strategic agent(s) behaviors through adaptively adjusting and estimating deployed strategies.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US18/290,707 US20250094855A1 (en) | 2021-07-21 | 2022-07-20 | Optimal data-driven decision-making in multi-agent systems |
Applications Claiming Priority (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| US202163224325P | 2021-07-21 | 2021-07-21 | |
| US63/224,325 | 2021-07-21 |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| WO2023003979A2 WO2023003979A2 (en) | 2023-01-26 |
| WO2023003979A3 true WO2023003979A3 (en) | 2023-02-23 |
Family
ID=84978747
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| PCT/US2022/037763 Ceased WO2023003979A2 (en) | 2021-07-21 | 2022-07-20 | Optimal data-driven decision-making in multi-agent systems |
Country Status (2)
| Country | Link |
|---|---|
| US (1) | US20250094855A1 (en) |
| WO (1) | WO2023003979A2 (en) |
Families Citing this family (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20240070523A1 (en) * | 2022-08-23 | 2024-02-29 | Microsoft Technology Licensing, Llc | Machine learning training content delivery |
| CN116843198B (en) * | 2023-07-04 | 2025-08-12 | 西北工业大学 | A dynamic human-machine function allocation method for manned submersibles based on non-cooperative game |
| WO2025043050A1 (en) * | 2023-08-24 | 2025-02-27 | Carnegie Mellon University | Training and use of a posture invariant brain-computer interface |
| CN117518833B (en) * | 2023-12-20 | 2024-05-31 | 哈尔滨工业大学 | Improved high-order multi-autonomous cluster distributed non-cooperative game method and system |
| CN117521716B (en) * | 2024-01-02 | 2024-03-19 | 山东大学 | Collaborative decision-making methods and media for massive unknown options and limited memory space |
| WO2025166403A1 (en) * | 2024-02-05 | 2025-08-14 | Strategex Pty Ltd | Multi-agent computer control system with iterative optimisation for autonomous agent coordination |
| CN118538362B (en) * | 2024-04-08 | 2024-12-27 | 南京信息工程大学 | Somatosensory-based interactive virtual rehabilitation training method and system |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170334066A1 (en) * | 2016-05-20 | 2017-11-23 | Google Inc. | Machine learning methods and apparatus related to predicting motion(s) of object(s) in a robot's environment based on image(s) capturing the object(s) and based on parameter(s) for future robot movement in the environment |
| US20180183827A1 (en) * | 2016-12-28 | 2018-06-28 | Palantir Technologies Inc. | Resource-centric network cyber attack warning system |
| US20190102692A1 (en) * | 2017-09-29 | 2019-04-04 | Here Global B.V. | Method, apparatus, and system for quantifying a diversity in a machine learning training data set |
| US20190310636A1 (en) * | 2018-04-09 | 2019-10-10 | SafeAl, Inc. | Dynamically controlling sensor behavior |
| US20200298100A1 (en) * | 2019-03-21 | 2020-09-24 | Valve Corporation | Brain-computer interfaces for computing systems |
-
2022
- 2022-07-20 WO PCT/US2022/037763 patent/WO2023003979A2/en not_active Ceased
- 2022-07-20 US US18/290,707 patent/US20250094855A1/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20170334066A1 (en) * | 2016-05-20 | 2017-11-23 | Google Inc. | Machine learning methods and apparatus related to predicting motion(s) of object(s) in a robot's environment based on image(s) capturing the object(s) and based on parameter(s) for future robot movement in the environment |
| US20180183827A1 (en) * | 2016-12-28 | 2018-06-28 | Palantir Technologies Inc. | Resource-centric network cyber attack warning system |
| US20190102692A1 (en) * | 2017-09-29 | 2019-04-04 | Here Global B.V. | Method, apparatus, and system for quantifying a diversity in a machine learning training data set |
| US20190310636A1 (en) * | 2018-04-09 | 2019-10-10 | SafeAl, Inc. | Dynamically controlling sensor behavior |
| US20200298100A1 (en) * | 2019-03-21 | 2020-09-24 | Valve Corporation | Brain-computer interfaces for computing systems |
Also Published As
| Publication number | Publication date |
|---|---|
| US20250094855A1 (en) | 2025-03-20 |
| WO2023003979A2 (en) | 2023-01-26 |
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